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Scene Parsing
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Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning
Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models
Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection
Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
Tactical Decision for Multi-UGV Confrontation with a Vision-Language Model-Based Commander
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis
EmbRACE-3K: Embodied Reasoning and Action in Complex Environments
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video
IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals
HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions
DIP: Unsupervised Dense In-Context Post-training of Visual Representations
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems
SceneAware: Scene-Constrained Pedestrian Trajectory Prediction with LLM-Guided Walkability
Unified Representation Space for 3D Visual Grounding
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding
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